Overview

Dataset statistics

Number of variables27
Number of observations5000
Missing cells589
Missing cells (%)0.4%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1020.6 KiB
Average record size in memory209.0 B

Variable types

Numeric21
Categorical3
Boolean3

Alerts

IsRetweet has constant value "False" Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
Text has a high cardinality: 4962 distinct values High cardinality
SenderLocation has a high cardinality: 426 distinct values High cardinality
Id is highly correlated with Retweets# and 3 other fieldsHigh correlation
Retweets# is highly correlated with Id and 3 other fieldsHigh correlation
Favorites# is highly correlated with Id and 3 other fieldsHigh correlation
SenderId is highly correlated with SenderAccountYears and 1 other fieldsHigh correlation
SenderAccountYears is highly correlated with SenderId and 1 other fieldsHigh correlation
SenderFavorites# is highly correlated with SenderFollowings# and 1 other fieldsHigh correlation
SenderFollowings# is highly correlated with SenderFavorites#High correlation
SenderFollowers# is highly correlated with Retweets# and 2 other fieldsHigh correlation
SenderStatues# is highly correlated with SenderId and 3 other fieldsHigh correlation
Punctuations# is highly correlated with UpperCaseLetter# and 3 other fieldsHigh correlation
UpperCaseLetter# is highly correlated with Punctuations# and 2 other fieldsHigh correlation
Letter# is highly correlated with Punctuations# and 2 other fieldsHigh correlation
Words# is highly correlated with Punctuations# and 2 other fieldsHigh correlation
TWords# is highly correlated with Punctuations# and 3 other fieldsHigh correlation
UWords# is highly correlated with UpperCaseLetter#High correlation
SlangWords# is highly correlated with Id and 1 other fieldsHigh correlation
IsCyberbullying is highly correlated with Id and 3 other fieldsHigh correlation
Retweets# is highly correlated with Favorites#High correlation
Favorites# is highly correlated with Retweets#High correlation
Punctuations# is highly correlated with Letter# and 1 other fieldsHigh correlation
UpperCaseLetter# is highly correlated with TWords# and 1 other fieldsHigh correlation
Letter# is highly correlated with Punctuations# and 2 other fieldsHigh correlation
Words# is highly correlated with Punctuations# and 2 other fieldsHigh correlation
TWords# is highly correlated with UpperCaseLetter# and 3 other fieldsHigh correlation
UWords# is highly correlated with UpperCaseLetter# and 1 other fieldsHigh correlation
SlangWords# is highly correlated with IsCyberbullyingHigh correlation
IsCyberbullying is highly correlated with SlangWords#High correlation
Id is highly correlated with IsCyberbullyingHigh correlation
Retweets# is highly correlated with Favorites# and 2 other fieldsHigh correlation
Favorites# is highly correlated with Retweets# and 1 other fieldsHigh correlation
SenderId is highly correlated with SenderAccountYearsHigh correlation
SenderAccountYears is highly correlated with SenderIdHigh correlation
SenderFollowers# is highly correlated with Retweets# and 1 other fieldsHigh correlation
UpperCaseLetter# is highly correlated with TWords#High correlation
Letter# is highly correlated with Words#High correlation
Words# is highly correlated with Letter#High correlation
TWords# is highly correlated with UpperCaseLetter#High correlation
SlangWords# is highly correlated with IsCyberbullyingHigh correlation
IsCyberbullying is highly correlated with Id and 2 other fieldsHigh correlation
IsCyberbullying is highly correlated with IsRetweetHigh correlation
IsRetweet is highly correlated with IsCyberbullying and 2 other fieldsHigh correlation
Medias# is highly correlated with IsRetweetHigh correlation
IsSelfMentioned is highly correlated with IsRetweetHigh correlation
Id is highly correlated with Retweets#High correlation
Retweets# is highly correlated with Id and 1 other fieldsHigh correlation
Favorites# is highly correlated with Retweets#High correlation
UpperCaseLetter# is highly correlated with TWords# and 1 other fieldsHigh correlation
Letter# is highly correlated with Words# and 1 other fieldsHigh correlation
Words# is highly correlated with Letter# and 1 other fieldsHigh correlation
TWords# is highly correlated with UpperCaseLetter# and 3 other fieldsHigh correlation
UWords# is highly correlated with UpperCaseLetter# and 1 other fieldsHigh correlation
SlangWords# is highly correlated with IsCyberbullyingHigh correlation
IsCyberbullying is highly correlated with SlangWords#High correlation
SenderLocation has 364 (7.3%) missing values Missing
AvgWordLength has 170 (3.4%) missing values Missing
SenderAccountYears is highly skewed (γ1 = 32.60134166) Skewed
Text is uniformly distributed Uniform
Retweets# has 2862 (57.2%) zeros Zeros
Favorites# has 2457 (49.1%) zeros Zeros
Hashtags# has 4426 (88.5%) zeros Zeros
Mentions# has 3244 (64.9%) zeros Zeros
SenderAccountYears has 1201 (24.0%) zeros Zeros
SenderFavorites# has 175 (3.5%) zeros Zeros
SenderFollowings# has 130 (2.6%) zeros Zeros
SenderFollowers# has 132 (2.6%) zeros Zeros
Emojis# has 4286 (85.7%) zeros Zeros
Punctuations# has 1954 (39.1%) zeros Zeros
UpperCaseLetter# has 668 (13.4%) zeros Zeros
Symbols# has 4771 (95.4%) zeros Zeros
TWords# has 675 (13.5%) zeros Zeros
UWords# has 4394 (87.9%) zeros Zeros
SlangWords# has 2206 (44.1%) zeros Zeros

Reproduction

Analysis started2022-01-04 08:50:02.445036
Analysis finished2022-01-04 08:51:50.596895
Duration1 minute and 48.15 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1465
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.149112634 × 1018
Minimum1292782403
Maximum1.2073 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:50.762850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1292782403
5-th percentile1.085241 × 1018
Q11.15814 × 1018
median1.15927 × 1018
Q31.16201 × 1018
95-th percentile1.20725 × 1018
Maximum1.2073 × 1018
Range1.207299999 × 1018
Interquartile range (IQR)3.87 × 1015

Descriptive statistics

Standard deviation8.370279448 × 1016
Coefficient of variation (CV)0.07284124462
Kurtosis78.85631493
Mean1.149112634 × 1018
Median Absolute Deviation (MAD)1.93 × 1015
Skewness-7.825538008
Sum5.745563171 × 1021
Variance7.006157804 × 1033
MonotonicityNot monotonic
2022-01-04T09:51:51.036718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.15839 × 1018168
 
3.4%
1.15843 × 1018149
 
3.0%
1.15838 × 1018103
 
2.1%
1.20728 × 101896
 
1.9%
1.1584 × 101886
 
1.7%
1.20727 × 101863
 
1.3%
1.15844 × 101849
 
1.0%
1.20725 × 101834
 
0.7%
1.20726 × 101830
 
0.6%
1.15837 × 101828
 
0.6%
Other values (1455)4194
83.9%
ValueCountFrequency (%)
12927824031
< 0.1%
2.288804507 × 10101
< 0.1%
2.664014936 × 10101
< 0.1%
2.54852 × 10161
< 0.1%
6.91767 × 10161
< 0.1%
7.30291 × 10161
< 0.1%
8.1419 × 10161
< 0.1%
1.07941 × 10171
< 0.1%
1.32155 × 10171
< 0.1%
1.54195 × 10171
< 0.1%
ValueCountFrequency (%)
1.2073 × 101822
 
0.4%
1.20729 × 101815
 
0.3%
1.20728 × 101896
1.9%
1.20727 × 101863
1.3%
1.20726 × 101830
 
0.6%
1.20725 × 101834
 
0.7%
1.20724 × 101814
 
0.3%
1.20723 × 101824
 
0.5%
1.20722 × 10189
 
0.2%
1.20721 × 101813
 
0.3%

Text
Categorical

HIGH CARDINALITY
UNIFORM

Distinct4962
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
merhaba ben kayseri den travesti hasret sevda görüşmeleri mi kendime ait evimde yapıyorum ne aradıgını biliyorsan ve ciddiysen eğer görüşelim müsaitim şuan 0541 691 29 19 bayan degilim 0541 691 29 19
 
5
piç herif
 
3
neşet ertaş elini kalbine götürdü burası varya dedi taşa toprağa gerzek kalmadan insanın gömüldüğü tek yer
 
3
hoÅŸt
 
3
bursatravesti sınırsız oldu bitti yok bursa altıparmak travesti afra 05366906903
 
3
Other values (4957)
4983 

Length

Max length320
Median length77
Mean length103.2358
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4932 ?
Unique (%)98.6%

Sample

1st rowbir adam yanında çocuklaşan kadını fazladan sevmeli çünkü bu yalnızken hep güçlü göründüm izninle huzur bulduğum yerde biraz şımarmak istiyorum deme şeklidir perşembe
2nd rowmağlup mu desem mahcup mu ama ikisi de değil ben garip sen güzel dünya umutlu öyle bir tuhafım bu akşamüstü sevgilim canavar götünü gibi iki yanım iki süngü ahmed arif perşembe
3rd rowgünaydın iyi pazarlar allah acil şifalar versin inşallah daha da iyi olacak
4th rowve ahmet arif leyla sına seslenir sevdiğim çaresizliğimden gayrı hiçbir kabahatim yok benim aşına ekmeğine kahrına karanlığına özlemine umuduna kat beni pazar hayalettimde
5th rowarkadaki sanal gerzek oyunun oynuyor

Common Values

ValueCountFrequency (%)
merhaba ben kayseri den travesti hasret sevda görüşmeleri mi kendime ait evimde yapıyorum ne aradıgını biliyorsan ve ciddiysen eğer görüşelim müsaitim şuan 0541 691 29 19 bayan degilim 0541 691 29 195
 
0.1%
piç herif3
 
0.1%
neşet ertaş elini kalbine götürdü burası varya dedi taşa toprağa gerzek kalmadan insanın gömüldüğü tek yer3
 
0.1%
hoÅŸt3
 
0.1%
bursatravesti sınırsız oldu bitti yok bursa altıparmak travesti afra 053669069033
 
0.1%
siz kahpe ölmediyseniz bize dertten bir şey olmaz merak etmeyin3
 
0.1%
interpol ün aradığı cip suriye ye götünü istenirken ele geçirildi2
 
< 0.1%
ben çevrem genişlesin diye orospu çocuklarının yüzüne gülmem2
 
< 0.1%
gunaydın2
 
< 0.1%
orospu evladı2
 
< 0.1%
Other values (4952)4972
99.4%

Length

2022-01-04T09:51:51.321387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bir970
 
1.4%
bu776
 
1.1%
ve686
 
1.0%
ne527
 
0.7%
da433
 
0.6%
de397
 
0.6%
iã§in387
 
0.6%
aq351
 
0.5%
ben322
 
0.5%
var316
 
0.4%
Other values (23663)65190
92.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IsRetweet
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Memory size39.2 KiB
False
4995 
(Missing)
 
5
ValueCountFrequency (%)
False4995
99.9%
(Missing)5
 
0.1%
2022-01-04T09:51:51.479550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

IsSelfMentioned
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)0.1%
Memory size39.2 KiB
False
4990 
True
 
4
(Missing)
 
6
ValueCountFrequency (%)
False4990
99.8%
True4
 
0.1%
(Missing)6
 
0.1%
2022-01-04T09:51:51.545382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Retweets#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct471
Distinct (%)9.5%
Missing33
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean132.1044896
Minimum0
Maximum29086
Zeros2862
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:51.696223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile302.7
Maximum29086
Range29086
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1078.051319
Coefficient of variation (CV)8.160595613
Kurtosis368.8622646
Mean132.1044896
Median Absolute Deviation (MAD)0
Skewness17.41103037
Sum656163
Variance1162194.645
MonotonicityNot monotonic
2022-01-04T09:51:51.921267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02862
57.2%
1412
 
8.2%
2149
 
3.0%
3103
 
2.1%
465
 
1.3%
564
 
1.3%
754
 
1.1%
650
 
1.0%
1039
 
0.8%
837
 
0.7%
Other values (461)1132
 
22.6%
(Missing)33
 
0.7%
ValueCountFrequency (%)
02862
57.2%
1412
 
8.2%
2149
 
3.0%
3103
 
2.1%
465
 
1.3%
564
 
1.3%
650
 
1.0%
754
 
1.1%
837
 
0.7%
922
 
0.4%
ValueCountFrequency (%)
290861
< 0.1%
287351
< 0.1%
245171
< 0.1%
205301
< 0.1%
196481
< 0.1%
192551
< 0.1%
190741
< 0.1%
159961
< 0.1%
152791
< 0.1%
118681
< 0.1%

Favorites#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct884
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733.0774
Minimum0
Maximum163984
Zeros2457
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:52.168513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q344
95-th percentile2388.5
Maximum163984
Range163984
Interquartile range (IQR)44

Descriptive statistics

Standard deviation4955.513189
Coefficient of variation (CV)6.759877183
Kurtosis385.8352734
Mean733.0774
Median Absolute Deviation (MAD)1
Skewness16.6144198
Sum3665387
Variance24557110.97
MonotonicityNot monotonic
2022-01-04T09:51:52.404536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02457
49.1%
1382
 
7.6%
2126
 
2.5%
394
 
1.9%
467
 
1.3%
548
 
1.0%
636
 
0.7%
736
 
0.7%
835
 
0.7%
1629
 
0.6%
Other values (874)1690
33.8%
ValueCountFrequency (%)
02457
49.1%
1382
 
7.6%
2126
 
2.5%
394
 
1.9%
467
 
1.3%
548
 
1.0%
636
 
0.7%
736
 
0.7%
835
 
0.7%
928
 
0.6%
ValueCountFrequency (%)
1639841
< 0.1%
1138981
< 0.1%
941261
< 0.1%
863751
< 0.1%
843721
< 0.1%
812121
< 0.1%
702621
< 0.1%
571851
< 0.1%
515181
< 0.1%
464651
< 0.1%

Hashtags#
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2032
Minimum0
Maximum23
Zeros4426
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:52.910316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.932139226
Coefficient of variation (CV)4.587299341
Kurtosis221.4808724
Mean0.2032
Median Absolute Deviation (MAD)0
Skewness12.4027872
Sum1016
Variance0.8688835367
MonotonicityNot monotonic
2022-01-04T09:51:53.087312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
04426
88.5%
1399
 
8.0%
295
 
1.9%
342
 
0.8%
411
 
0.2%
58
 
0.2%
74
 
0.1%
63
 
0.1%
92
 
< 0.1%
182
 
< 0.1%
Other values (8)8
 
0.2%
ValueCountFrequency (%)
04426
88.5%
1399
 
8.0%
295
 
1.9%
342
 
0.8%
411
 
0.2%
58
 
0.2%
63
 
0.1%
74
 
0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
231
< 0.1%
201
< 0.1%
182
< 0.1%
171
< 0.1%
161
< 0.1%
121
< 0.1%
111
< 0.1%
101
< 0.1%
92
< 0.1%
81
< 0.1%

Medias#
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0
3920 
1
901 
2
 
88
4
 
67
3
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03920
78.4%
1901
 
18.0%
288
 
1.8%
467
 
1.3%
324
 
0.5%

Length

2022-01-04T09:51:53.260956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-04T09:51:53.373780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
03920
78.4%
1901
 
18.0%
288
 
1.8%
467
 
1.3%
324
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mentions#
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5614
Minimum0
Maximum50
Zeros3244
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:53.488032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum50
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.654804434
Coefficient of variation (CV)2.947638821
Kurtosis468.6157982
Mean0.5614
Median Absolute Deviation (MAD)0
Skewness17.74017422
Sum2807
Variance2.738377716
MonotonicityNot monotonic
2022-01-04T09:51:53.675020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
03244
64.9%
11294
 
25.9%
2286
 
5.7%
3102
 
2.0%
427
 
0.5%
512
 
0.2%
69
 
0.2%
104
 
0.1%
84
 
0.1%
73
 
0.1%
Other values (13)15
 
0.3%
ValueCountFrequency (%)
03244
64.9%
11294
 
25.9%
2286
 
5.7%
3102
 
2.0%
427
 
0.5%
512
 
0.2%
69
 
0.2%
73
 
0.1%
84
 
0.1%
93
 
0.1%
ValueCountFrequency (%)
501
< 0.1%
491
< 0.1%
481
< 0.1%
291
< 0.1%
191
< 0.1%
171
< 0.1%
161
< 0.1%
151
< 0.1%
141
< 0.1%
131
< 0.1%

SenderId
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct4167
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.62601359 × 1017
Minimum3696241
Maximum1.20707 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:53.895883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3696241
5-th percentile124726283
Q11208505124
median8.02847 × 1017
Q31.06708 × 1018
95-th percentile1.1583415 × 1018
Maximum1.20707 × 1018
Range1.20707 × 1018
Interquartile range (IQR)1.067079999 × 1018

Descriptive statistics

Standard deviation5.140665155 × 1017
Coefficient of variation (CV)0.9137313788
Kurtosis-1.873239105
Mean5.62601359 × 1017
Median Absolute Deviation (MAD)3.54858 × 1017
Skewness-0.1152238405
Sum2.813006795 × 1021
Variance2.642643823 × 1035
MonotonicityNot monotonic
2022-01-04T09:51:54.145887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
460413849426
 
0.5%
1.09486 × 101822
 
0.4%
1.00988 × 101815
 
0.3%
2318607914
 
0.3%
42380591510
 
0.2%
680344319
 
0.2%
1.09997 × 10189
 
0.2%
7.33373 × 10178
 
0.2%
711084768
 
0.2%
1.19244 × 10188
 
0.2%
Other values (4157)4871
97.4%
ValueCountFrequency (%)
36962411
< 0.1%
44959311
< 0.1%
150162091
< 0.1%
158832371
< 0.1%
163103191
< 0.1%
166269561
< 0.1%
188712131
< 0.1%
191490881
< 0.1%
199421682
< 0.1%
206725831
< 0.1%
ValueCountFrequency (%)
1.20707 × 10181
 
< 0.1%
1.20704 × 10181
 
< 0.1%
1.20703 × 10181
 
< 0.1%
1.207 × 10181
 
< 0.1%
1.20664 × 10181
 
< 0.1%
1.20655 × 10181
 
< 0.1%
1.20653 × 10184
0.1%
1.20634 × 10181
 
< 0.1%
1.2056 × 10181
 
< 0.1%
1.20439 × 10181
 
< 0.1%

SenderAccountYears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.342
Minimum0
Maximum2020
Zeros1201
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:54.362525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile9
Maximum2020
Range2020
Interquartile range (IQR)5

Descriptive statistics

Standard deviation59.55636681
Coefficient of variation (CV)11.14870214
Kurtosis1078.461339
Mean5.342
Median Absolute Deviation (MAD)3
Skewness32.60134166
Sum26710
Variance3546.960828
MonotonicityNot monotonic
2022-01-04T09:51:54.544082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
01201
24.0%
1790
15.8%
2493
9.9%
8374
 
7.5%
3366
 
7.3%
6361
 
7.2%
7357
 
7.1%
4329
 
6.6%
5283
 
5.7%
9250
 
5.0%
Other values (5)196
 
3.9%
ValueCountFrequency (%)
01201
24.0%
1790
15.8%
2493
9.9%
3366
 
7.3%
4329
 
6.6%
5283
 
5.7%
6361
 
7.2%
7357
 
7.1%
8374
 
7.5%
9250
 
5.0%
ValueCountFrequency (%)
20204
 
0.1%
12001
 
< 0.1%
122
 
< 0.1%
114
 
0.1%
10185
3.7%
9250
5.0%
8374
7.5%
7357
7.1%
6361
7.2%
5283
5.7%

SenderFavorites#
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3356
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20832.5534
Minimum0
Maximum814597
Zeros175
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:54.769217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1575.25
median3871
Q316760.25
95-th percentile91890
Maximum814597
Range814597
Interquartile range (IQR)16185

Descriptive statistics

Standard deviation53253.03531
Coefficient of variation (CV)2.556241393
Kurtosis49.69667545
Mean20832.5534
Median Absolute Deviation (MAD)3815
Skewness5.9199112
Sum104162767
Variance2835885770
MonotonicityNot monotonic
2022-01-04T09:51:55.028578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0175
 
3.5%
134
 
0.7%
233
 
0.7%
429
 
0.6%
35728
 
0.6%
160322
 
0.4%
520
 
0.4%
718
 
0.4%
316
 
0.3%
6716
 
0.3%
Other values (3346)4609
92.2%
ValueCountFrequency (%)
0175
3.5%
134
 
0.7%
233
 
0.7%
316
 
0.3%
429
 
0.6%
520
 
0.4%
611
 
0.2%
718
 
0.4%
812
 
0.2%
914
 
0.3%
ValueCountFrequency (%)
8145972
< 0.1%
6873551
< 0.1%
6407501
< 0.1%
5645491
< 0.1%
5144911
< 0.1%
5096361
< 0.1%
4892521
< 0.1%
4804561
< 0.1%
4457962
< 0.1%
4420571
< 0.1%

SenderFollowings#
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1659
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6714.929
Minimum0
Maximum1658816
Zeros130
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:55.301826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q197
median297
Q3955
95-th percentile8675.05
Maximum1658816
Range1658816
Interquartile range (IQR)858

Descriptive statistics

Standard deviation65110.99435
Coefficient of variation (CV)9.696453135
Kurtosis237.7327406
Mean6714.929
Median Absolute Deviation (MAD)248
Skewness14.5559893
Sum33574645
Variance4239441586
MonotonicityNot monotonic
2022-01-04T09:51:55.537117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130
 
2.6%
12100
 
2.0%
14929
 
0.6%
228
 
0.6%
9526
 
0.5%
50624
 
0.5%
5021
 
0.4%
421
 
0.4%
7120
 
0.4%
519
 
0.4%
Other values (1649)4582
91.6%
ValueCountFrequency (%)
0130
2.6%
119
 
0.4%
228
 
0.6%
319
 
0.4%
421
 
0.4%
519
 
0.4%
615
 
0.3%
713
 
0.3%
812
 
0.2%
913
 
0.3%
ValueCountFrequency (%)
16588161
 
< 0.1%
11632624
0.1%
8617451
 
< 0.1%
7977806
0.1%
7950253
0.1%
7355794
0.1%
6992837
0.1%
5576252
 
< 0.1%
3771431
 
< 0.1%
2844441
 
< 0.1%

SenderFollowers#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2262
Distinct (%)45.3%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean109699.399
Minimum0
Maximum13974880
Zeros132
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:55.792398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1123.5
median616
Q34881
95-th percentile207664
Maximum13974880
Range13974880
Interquartile range (IQR)4757.5

Descriptive statistics

Standard deviation836945.1911
Coefficient of variation (CV)7.6294419
Kurtosis171.4437323
Mean109699.399
Median Absolute Deviation (MAD)594
Skewness12.3426694
Sum547948498
Variance7.00477253 × 1011
MonotonicityNot monotonic
2022-01-04T09:51:56.030818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0132
 
2.6%
129
 
0.6%
64070126
 
0.5%
36025
 
0.5%
4625
 
0.5%
1024
 
0.5%
423
 
0.5%
223
 
0.5%
1920
 
0.4%
620
 
0.4%
Other values (2252)4648
93.0%
ValueCountFrequency (%)
0132
2.6%
129
 
0.6%
223
 
0.5%
320
 
0.4%
423
 
0.5%
518
 
0.4%
620
 
0.4%
719
 
0.4%
812
 
0.2%
916
 
0.3%
ValueCountFrequency (%)
139748809
0.2%
863149614
0.3%
71317541
 
< 0.1%
67312541
 
< 0.1%
67039946
0.1%
65081781
 
< 0.1%
49301353
 
0.1%
48952071
 
< 0.1%
36369982
 
< 0.1%
33193231
 
< 0.1%

SenderStatues#
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3004
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10745.4382
Minimum0
Maximum986428
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:56.295571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q1394
median1978
Q37052
95-th percentile43969.6
Maximum986428
Range986428
Interquartile range (IQR)6658

Descriptive statistics

Standard deviation38135.94718
Coefficient of variation (CV)3.549036016
Kurtosis213.8727319
Mean10745.4382
Median Absolute Deviation (MAD)1851
Skewness11.75945751
Sum53727191
Variance1454350468
MonotonicityNot monotonic
2022-01-04T09:51:56.537011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142
 
0.8%
191626
 
0.5%
223
 
0.5%
234122
 
0.4%
1318
 
0.4%
318
 
0.4%
1717
 
0.3%
1416
 
0.3%
135915
 
0.3%
614
 
0.3%
Other values (2994)4789
95.8%
ValueCountFrequency (%)
04
 
0.1%
142
0.8%
223
0.5%
318
0.4%
414
 
0.3%
510
 
0.2%
614
 
0.3%
79
 
0.2%
812
 
0.2%
913
 
0.3%
ValueCountFrequency (%)
9864282
 
< 0.1%
5260441
 
< 0.1%
4779921
 
< 0.1%
4536771
 
< 0.1%
4225861
 
< 0.1%
4106672
 
< 0.1%
3398678
0.2%
3323691
 
< 0.1%
3300941
 
< 0.1%
3253801
 
< 0.1%

SenderLocation
Categorical

HIGH CARDINALITY
MISSING

Distinct426
Distinct (%)9.2%
Missing364
Missing (%)7.3%
Memory size39.2 KiB
türkiye
798 
ankara
613 
istanbul
374 
bursa
 
226
antalya
 
201
Other values (421)
2424 

Length

Max length31
Median length7
Mean length7.114969802
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)5.8%

Sample

1st rowna
2nd rowna
3rd rowturkey
4th rowistanbul
5th rowturkey

Common Values

ValueCountFrequency (%)
türkiye798
16.0%
ankara613
 
12.3%
istanbul374
 
7.5%
bursa226
 
4.5%
antalya201
 
4.0%
adana161
 
3.2%
turkey141
 
2.8%
mersin103
 
2.1%
eskiÅŸehir102
 
2.0%
samsun82
 
1.6%
Other values (416)1835
36.7%
(Missing)364
 
7.3%

Length

2022-01-04T09:51:56.786856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tã¼rkiye798
17.2%
ankara613
 
13.2%
istanbul374
 
8.1%
bursa226
 
4.9%
antalya201
 
4.3%
adana161
 
3.5%
turkey141
 
3.0%
mersin103
 
2.2%
eskiåÿehir102
 
2.2%
samsun82
 
1.8%
Other values (416)1835
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Emojis#
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)0.4%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.3964757709
Minimum0
Maximum55
Zeros4286
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:56.982126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.683259428
Coefficient of variation (CV)4.245554335
Kurtosis288.7410113
Mean0.3964757709
Median Absolute Deviation (MAD)0
Skewness12.73367316
Sum1980
Variance2.833362302
MonotonicityNot monotonic
2022-01-04T09:51:57.152618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
04286
85.7%
1315
 
6.3%
2145
 
2.9%
3100
 
2.0%
461
 
1.2%
623
 
0.5%
515
 
0.3%
99
 
0.2%
128
 
0.2%
87
 
0.1%
Other values (11)25
 
0.5%
(Missing)6
 
0.1%
ValueCountFrequency (%)
04286
85.7%
1315
 
6.3%
2145
 
2.9%
3100
 
2.0%
461
 
1.2%
515
 
0.3%
623
 
0.5%
75
 
0.1%
87
 
0.1%
99
 
0.2%
ValueCountFrequency (%)
551
 
< 0.1%
292
 
< 0.1%
281
 
< 0.1%
211
 
< 0.1%
181
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
144
0.1%
128
0.2%
113
 
0.1%

Punctuations#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5278
Minimum0
Maximum84
Zeros1954
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:57.357468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum84
Range84
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.033122906
Coefficient of variation (CV)1.595507123
Kurtosis83.91174302
Mean2.5278
Median Absolute Deviation (MAD)1
Skewness6.076941003
Sum12639
Variance16.26608038
MonotonicityNot monotonic
2022-01-04T09:51:57.560748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
01954
39.1%
1765
 
15.3%
2546
 
10.9%
3428
 
8.6%
4334
 
6.7%
5231
 
4.6%
6184
 
3.7%
7149
 
3.0%
8108
 
2.2%
977
 
1.5%
Other values (27)224
 
4.5%
ValueCountFrequency (%)
01954
39.1%
1765
 
15.3%
2546
 
10.9%
3428
 
8.6%
4334
 
6.7%
5231
 
4.6%
6184
 
3.7%
7149
 
3.0%
8108
 
2.2%
977
 
1.5%
ValueCountFrequency (%)
842
< 0.1%
531
 
< 0.1%
431
 
< 0.1%
411
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
363
0.1%
341
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%

UpperCaseLetter#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.274
Minimum0
Maximum239
Zeros668
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:57.786047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile16
Maximum239
Range239
Interquartile range (IQR)3

Descriptive statistics

Standard deviation10.68225379
Coefficient of variation (CV)2.499357462
Kurtosis171.57481
Mean4.274
Median Absolute Deviation (MAD)1
Skewness10.45974138
Sum21370
Variance114.1105461
MonotonicityNot monotonic
2022-01-04T09:51:58.036687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12062
41.2%
0668
 
13.4%
2587
 
11.7%
3305
 
6.1%
4269
 
5.4%
5225
 
4.5%
6135
 
2.7%
793
 
1.9%
882
 
1.6%
975
 
1.5%
Other values (70)499
 
10.0%
ValueCountFrequency (%)
0668
 
13.4%
12062
41.2%
2587
 
11.7%
3305
 
6.1%
4269
 
5.4%
5225
 
4.5%
6135
 
2.7%
793
 
1.9%
882
 
1.6%
975
 
1.5%
ValueCountFrequency (%)
2391
< 0.1%
2351
< 0.1%
2251
< 0.1%
1951
< 0.1%
1361
< 0.1%
1291
< 0.1%
1281
< 0.1%
1201
< 0.1%
1001
< 0.1%
991
< 0.1%

Letter#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct244
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.1662
Minimum4
Maximum249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:58.290652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q135
median63
Q3116
95-th percentile220
Maximum249
Range245
Interquartile range (IQR)81

Descriptive statistics

Standard deviation62.52691341
Coefficient of variation (CV)0.7518308328
Kurtosis-0.08190286811
Mean83.1662
Median Absolute Deviation (MAD)34
Skewness0.9921842616
Sum415831
Variance3909.614901
MonotonicityNot monotonic
2022-01-04T09:51:58.827912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3669
 
1.4%
4363
 
1.3%
2559
 
1.2%
2758
 
1.2%
3456
 
1.1%
2056
 
1.1%
3954
 
1.1%
3753
 
1.1%
3053
 
1.1%
5653
 
1.1%
Other values (234)4426
88.5%
ValueCountFrequency (%)
44
 
0.1%
513
 
0.3%
67
 
0.1%
711
 
0.2%
825
0.5%
921
0.4%
1030
0.6%
1129
0.6%
1233
0.7%
1326
0.5%
ValueCountFrequency (%)
2491
 
< 0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2451
 
< 0.1%
2433
 
0.1%
2421
 
< 0.1%
2416
0.1%
2407
0.1%
2396
0.1%
2389
0.2%

Symbols#
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0826
Minimum0
Maximum8
Zeros4771
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:59.027901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4747866094
Coefficient of variation (CV)5.748021906
Kurtosis94.59551993
Mean0.0826
Median Absolute Deviation (MAD)0
Skewness8.580401114
Sum413
Variance0.2254223245
MonotonicityNot monotonic
2022-01-04T09:51:59.189022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
04771
95.4%
1136
 
2.7%
255
 
1.1%
418
 
0.4%
39
 
0.2%
54
 
0.1%
64
 
0.1%
83
 
0.1%
ValueCountFrequency (%)
04771
95.4%
1136
 
2.7%
255
 
1.1%
39
 
0.2%
418
 
0.4%
54
 
0.1%
64
 
0.1%
83
 
0.1%
ValueCountFrequency (%)
83
 
0.1%
64
 
0.1%
54
 
0.1%
418
 
0.4%
39
 
0.2%
255
 
1.1%
1136
 
2.7%
04771
95.4%

Words#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.071
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:59.406937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median11
Q320
95-th percentile36
Maximum49
Range48
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.47002786
Coefficient of variation (CV)0.7440855558
Kurtosis0.1568164349
Mean14.071
Median Absolute Deviation (MAD)6
Skewness1.048269592
Sum70355
Variance109.6214833
MonotonicityNot monotonic
2022-01-04T09:51:59.659519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
6354
 
7.1%
5334
 
6.7%
7292
 
5.8%
4291
 
5.8%
8274
 
5.5%
9271
 
5.4%
10240
 
4.8%
3238
 
4.8%
11215
 
4.3%
12191
 
3.8%
Other values (38)2300
46.0%
ValueCountFrequency (%)
154
 
1.1%
2140
 
2.8%
3238
4.8%
4291
5.8%
5334
6.7%
6354
7.1%
7292
5.8%
8274
5.5%
9271
5.4%
10240
4.8%
ValueCountFrequency (%)
492
 
< 0.1%
472
 
< 0.1%
465
 
0.1%
455
 
0.1%
447
 
0.1%
4312
 
0.2%
4226
0.5%
4129
0.6%
4031
0.6%
3936
0.7%

TWords#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct35
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6872
Minimum0
Maximum40
Zeros675
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:51:59.888884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile9
Maximum40
Range40
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.638406761
Coefficient of variation (CV)1.353976913
Kurtosis21.12780574
Mean2.6872
Median Absolute Deviation (MAD)1
Skewness3.679395601
Sum13436
Variance13.23800376
MonotonicityNot monotonic
2022-01-04T09:52:00.115468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
12103
42.1%
0675
 
13.5%
2672
 
13.4%
3388
 
7.8%
4291
 
5.8%
5207
 
4.1%
6145
 
2.9%
7108
 
2.2%
886
 
1.7%
984
 
1.7%
Other values (25)241
 
4.8%
ValueCountFrequency (%)
0675
 
13.5%
12103
42.1%
2672
 
13.4%
3388
 
7.8%
4291
 
5.8%
5207
 
4.1%
6145
 
2.9%
7108
 
2.2%
886
 
1.7%
984
 
1.7%
ValueCountFrequency (%)
401
 
< 0.1%
392
 
< 0.1%
382
 
< 0.1%
361
 
< 0.1%
341
 
< 0.1%
313
0.1%
281
 
< 0.1%
272
 
< 0.1%
261
 
< 0.1%
256
0.1%

UWords#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3518
Minimum0
Maximum40
Zeros4394
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:52:00.335790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.74924233
Coefficient of variation (CV)4.972263588
Kurtosis204.0404334
Mean0.3518
Median Absolute Deviation (MAD)0
Skewness11.93275092
Sum1759
Variance3.05984873
MonotonicityNot monotonic
2022-01-04T09:52:00.527745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
04394
87.9%
1325
 
6.5%
2100
 
2.0%
349
 
1.0%
436
 
0.7%
620
 
0.4%
516
 
0.3%
816
 
0.3%
714
 
0.3%
104
 
0.1%
Other values (16)26
 
0.5%
ValueCountFrequency (%)
04394
87.9%
1325
 
6.5%
2100
 
2.0%
349
 
1.0%
436
 
0.7%
516
 
0.3%
620
 
0.4%
714
 
0.3%
816
 
0.3%
93
 
0.1%
ValueCountFrequency (%)
401
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
341
 
< 0.1%
251
 
< 0.1%
231
 
< 0.1%
191
 
< 0.1%
183
0.1%
171
 
< 0.1%
162
< 0.1%

SlangWords#
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7808
Minimum0
Maximum7
Zeros2206
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:52:00.718793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9141764073
Coefficient of variation (CV)1.170820194
Kurtosis4.82699131
Mean0.7808
Median Absolute Deviation (MAD)1
Skewness1.728787084
Sum3904
Variance0.8357185037
MonotonicityNot monotonic
2022-01-04T09:52:00.885847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
02206
44.1%
12042
40.8%
2531
 
10.6%
3126
 
2.5%
465
 
1.3%
520
 
0.4%
68
 
0.2%
72
 
< 0.1%
ValueCountFrequency (%)
02206
44.1%
12042
40.8%
2531
 
10.6%
3126
 
2.5%
465
 
1.3%
520
 
0.4%
68
 
0.2%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
68
 
0.2%
520
 
0.4%
465
 
1.3%
3126
 
2.5%
2531
 
10.6%
12042
40.8%
02206
44.1%

AvgWordLength
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.4%
Missing170
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean5.53747412
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-01-04T09:52:01.059224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum53
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.460511998
Coefficient of variation (CV)0.2637505776
Kurtosis233.5974868
Mean5.53747412
Median Absolute Deviation (MAD)1
Skewness8.007463239
Sum26746
Variance2.133095296
MonotonicityNot monotonic
2022-01-04T09:52:01.251236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
51817
36.3%
61403
28.1%
4683
 
13.7%
7510
 
10.2%
8176
 
3.5%
3117
 
2.3%
958
 
1.2%
1022
 
0.4%
1115
 
0.3%
210
 
0.2%
Other values (7)19
 
0.4%
(Missing)170
 
3.4%
ValueCountFrequency (%)
13
 
0.1%
210
 
0.2%
3117
 
2.3%
4683
 
13.7%
51817
36.3%
61403
28.1%
7510
 
10.2%
8176
 
3.5%
958
 
1.2%
1022
 
0.4%
ValueCountFrequency (%)
531
 
< 0.1%
162
 
< 0.1%
152
 
< 0.1%
141
 
< 0.1%
133
 
0.1%
127
 
0.1%
1115
 
0.3%
1022
 
0.4%
958
 
1.2%
8176
3.5%

IsCyberbullying
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
2500 
True
2500 
ValueCountFrequency (%)
False2500
50.0%
True2500
50.0%
2022-01-04T09:52:01.385934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Interactions

2022-01-04T09:51:43.098308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:04.969124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:10.110245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:15.052392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:20.164834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:24.873375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:29.864490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:34.829954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:40.187014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:45.510598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:50.317858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:55.449317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:00.090824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:04.906422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:09.321676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:14.179681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:18.800585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:23.819041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:28.609954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:33.381006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:38.415828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:43.314657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:05.202916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:10.359780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:15.278479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:20.374892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:25.092275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:30.087170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:35.350751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:40.438854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:45.752491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:50.560806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:55.667367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:00.308080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:05.123650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:09.539300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:14.388872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:19.331675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:24.035609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:28.835153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:33.642175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:38.640935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:43.531318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:05.428147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:10.645720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:15.509003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:20.651617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:25.305059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:30.314302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:35.604180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:40.670049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:46.029122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:50.784837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:55.888285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:00.524403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:05.331626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:09.755272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:14.605108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:19.553490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:24.249811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:29.051615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:33.860609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:38.857703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:43.748796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:05.702411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:10.868397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:15.759136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:20.866879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:25.556560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:30.539636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:35.845886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:40.902907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:46.261622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:51.018316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:56.108432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:00.740779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:05.522836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:09.972517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:14.821311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:19.780543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:24.460982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:29.286905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:34.083413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:39.082602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:43.948027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:05.902893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:11.076545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:15.967093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:21.058110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:25.765532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:30.782635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:36.062520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:41.128880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:46.469099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:51.234343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:56.324593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:00.933019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:05.698136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:10.464174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:15.038400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:19.986985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:24.668627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:29.493103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:34.300142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:39.288633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:44.160032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:06.119597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:11.293404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-04T09:51:17.287447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:22.244553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:26.817944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:31.783777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:36.866154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:41.532372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:46.622648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:08.752612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:13.667697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:18.766190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:23.532848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:28.480994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:33.347099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:38.745423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:43.760940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:48.970531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:54.042458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:58.749399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:03.606745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:08.039669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:12.854683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:17.494934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:22.461152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:27.018811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:32.000568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:37.082513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:41.740410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:46.842260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:08.979228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:13.900784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:18.993216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:23.773807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:28.731193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:33.646677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:38.987645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:44.299118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:49.202066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:54.292001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:58.974195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:03.823915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:08.256138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:13.071210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:17.720441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:22.686277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:27.234842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:32.233134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:37.316196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:41.981956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:47.047728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:09.197700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:14.118026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:19.210636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:23.977405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:28.939576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:33.871700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:39.212381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:44.527877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:49.410091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:54.509084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:59.191027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:04.031774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:08.456097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:13.288388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:17.920688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:22.904223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:27.735390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:32.450576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:37.490980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:42.198817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:47.272656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:09.427035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:14.342680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:19.450138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:24.190351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:29.164077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:34.104407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:39.467894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:44.802789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:49.634026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:54.750301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:59.424301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:04.256794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:08.672029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:13.513257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:18.145182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:23.136321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:27.959649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:32.675644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:37.724520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:42.423858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:47.497800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:09.676902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:14.592600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:19.716798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:24.406190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:29.389318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:34.338035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:39.728930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:45.044406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:49.868073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:54.992026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:59.649398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:04.481246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:08.889791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:13.739177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:18.370476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:23.377678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:28.185412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:32.909584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:37.964684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:42.657246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:47.719657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:09.901748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:14.825574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:19.944088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:24.665416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:29.656744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:34.604266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:39.962432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:45.285686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:50.099799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:55.225569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:50:59.874422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:04.698195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:09.114499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:13.964967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:18.587141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:23.602352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:28.401866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:33.150291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:38.192042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-04T09:51:42.882024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-04T09:52:01.573312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-04T09:52:02.094232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-04T09:52:02.617301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-04T09:52:03.076869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-04T09:52:03.393670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-04T09:51:48.212095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-04T09:51:49.555047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-04T09:51:50.005076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-04T09:51:50.304653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdTextIsRetweetIsSelfMentionedRetweets#Favorites#Hashtags#Medias#Mentions#SenderIdSenderAccountYearsSenderFavorites#SenderFollowings#SenderFollowers#SenderStatues#SenderLocationEmojis#Punctuations#UpperCaseLetter#Letter#Symbols#Words#TWords#UWords#SlangWords#AvgWordLengthIsCyberbullying
01.123850e+18bir adam yanında çocuklaşan kadını fazladan sevmeli çünkü bu yalnızken hep güçlü göründüm izninle huzur bulduğum yerde biraz şımarmak istiyorum deme şeklidir perşembeFalseFalse59.010451001.935601e+092020000.00NaN0.0951440235006.0False
11.161960e+18mağlup mu desem mahcup mu ama ikisi de değil ben garip sen güzel dünya umutlu öyle bir tuhafım bu akşamüstü sevgilim canavar götünü gibi iki yanım iki süngü ahmed arif perşembeFalseFalse3.01571001.935601e+092020000.00na0.0881470318014.0False
21.162600e+18günaydın iyi pazarlar allah acil şifalar versin inşallah daha da iyi olacakFalseFalse1.0300119.276140e+172020000.00NaN3.002642122005.0False
31.163020e+18ve ahmet arif leyla sına seslenir sevdiğim çaresizliğimden gayrı hiçbir kabahatim yok benim aşına ekmeğine kahrına karanlığına özlemine umuduna kat beni pazar hayalettimdeFalseFalse13.02202001.935601e+092020000.00na0.0191615002314006.0False
41.157730e+18arkadaki sanal gerzek oyunun oynuyorFalseFalse950.0121040104.495931e+0612225543276660281.025482turkey0.00135051017.0False
51.158390e+18ikea dolap montajında zorlanacağına eminimFalseFalseNaN00013.696241e+06121047457334.01853istanbul0.01138051007.0False
61.158380e+18marmaris te yüksek ses ve gürültüye 637 bin lira cezaFalseFalse3.0310101.501621e+07110397131754.0221392turkey0.011440101004.0False
71.158440e+18mercedes in takım patronu olsaydınız lewis hamilton ın takım arkadaşı olarak kim tercih ederdinizFalseFalse1.0130001.631032e+0711365114818548.068489turkey2.043840143006.0False
81.158680e+18bu çocuğu hala nasıl takip edebiliyorsunuz ya gösteriş budala resmennFalseFalseNaN00011.588324e+0711143438981985.02389ankara0.000670100016.0True
91.162630e+18pudingin soğumuş pürüzsüz yüzeyini öpmek zevk alınan ufak sapıkFalseFalse3.0390001.662696e+07111052653596.016833istanbul0.03061190016.0False

Last rows

IdTextIsRetweetIsSelfMentionedRetweets#Favorites#Hashtags#Medias#Mentions#SenderIdSenderAccountYearsSenderFavorites#SenderFollowings#SenderFollowers#SenderStatues#SenderLocationEmojis#Punctuations#UpperCaseLetter#Letter#Symbols#Words#TWords#UWords#SlangWords#AvgWordLengthIsCyberbullying
49901.207290e+18tipitip dallama iÅŸteFalseFalse0.000021.183810e+180474921762.0659NaN0.00120031016.0True
49911.207300e+18boş bulunma ortamında barındırma puştFalseFalse0.000001.161600e+1805478287.033mersin0.00034050016.0True
49921.207300e+18sen verme ak akÅŸam akÅŸam iti kopuÄŸu puÅŸt kuÅŸtu uÄŸraÅŸmayak amk kfkekeFalseFalse0.010011.207000e+180302414.035NaN0.012580122024.0True
49931.207300e+18sizinki iman falan da degil basbayağı orospu tamam mi vicdanına soktugumun pustuFalseFalse0.000021.119680e+18083632.0579NaN0.012720122016.0True
49941.207300e+18fahişe olmuş ruhların vesikayla ispatı yoktur vesikası olan kendini namuslu sanırmış çomarFalseFalse0.010021.121490e+18017176430251.05907NaN0.012790122016.0True
49951.207300e+18sakalını sktiğimin moron milleti korkutmak için emniyeti mentliyen bebe siz avrupadan gelen fonları cebe atacaksınız diye bu ülke vatandaşları her gün intihar ediyor sen adres ver ben senin ziyaretine gelirimFalseFalse0.000011.163550e+18022452836804.02460intikam0.0421800302016.0True
49961.207300e+18yavÅŸak salmadi hala serefsizFalseFalse0.000001.183450e+1809053208212.04174day60.00025040016.0True
49971.207300e+18bu şerefsiz i biraz açmak için seçildiğinde yemin etti şerefinin ve namusunun üzerine herkese cumhurbaşkanı olmak için yaptımı hayır onun için şerefsiz namussuz diplomasız işgal ettiği yer için yalancı sahtekâr dolandırıcı konuşmalarında yaptıklarında vatan haini rteFalseFalse0.000021.201560e+1801150.0104NaN0.0752320365006.0True
49981.207300e+18battal etmek fahişe gemleme bulaşıkhane tecavübFalseFalse0.000011.207070e+180000.0303NaN0.00042060017.0True
49991.207300e+18yine ortalık rahibe götünü fahişe kaynıyor bir yavaş öslxşeöodFalseFalse0.070001.184090e+180286651676.060adana0.00157091026.0True

Duplicate rows

Most frequently occurring

IdTextIsRetweetIsSelfMentionedRetweets#Favorites#Hashtags#Medias#Mentions#SenderIdSenderAccountYearsSenderFavorites#SenderFollowings#SenderFollowers#SenderStatues#SenderLocationEmojis#Punctuations#UpperCaseLetter#Letter#Symbols#Words#TWords#UWords#SlangWords#AvgWordLengthIsCyberbullying# duplicates
01.160730e+18merhaba ben kayseri den travesti hasret sevda görüşmeleri mi kendime ait evimde yapıyorum ne aradıgını biliyorsan ve ciddiysen eğer görüşelim müsaitim şuan 0541 691 29 19 bayan degilim 0541 691 29 19FalseFalse0.000107.315140e+1739118152533.06545kayseri0.09181680328215.0True2
11.207280e+18seni santim santim öperek sokarım sonra her milimini yalar amına dil atar zevke getiririm altıma alıp bacaklarını omuzuma dayarım yarağımı amına sokup dibine kadar köklerim altımda tamamen kavrayıp sert sert hızlı hızlı haşin haşin bağırta bağırta inlete inlete sikerim dm yeFalseFalse0.000011.043120e+181480896690.01195istanbul0.0672350416145.0True2